How to use DistanceMetrix.pairwise() using haversine distance in scikit

Question

How do you get the distance in kilometers using the haversine pairwise function in sklearn library? Looking over the example at https://stackoverflow.com/a/38685263/8378399 the numbers returned from scikit-learn are not correct which leads me to believe I'm not calling it correctly.

Sample code:

from math import radians, cos, sin, asin, sqrt

def haversine(lon1, lat1, lon2, lat2):
    """
    Calculate the great circle distance between two points 
    on the earth (specified in decimal degrees)
    """
    # convert decimal degrees to radians 
    lon1, lat1, lon2, lat2 = map(radians, [lon1, lat1, lon2, lat2])

    # haversine formula 
    dlon = lon2 - lon1 
    dlat = lat2 - lat1 
    a = sin(dlat/2)**2 + cos(lat1) * cos(lat2) * sin(dlon/2)**2
    c = 2 * asin(sqrt(a)) 
    r = 6371 # Radius of earth in kilometers. Use 3956 for miles
    return c * r

from sklearn.neighbors import DistanceMetric
dist = DistanceMetric.get_metric('haversine')

paris = (48.8566, 2.3522)
lyon = (45.7640, 4.8357)

hdist = haversine(paris[1],paris[0], lyon[1], lyon[0])
skdist = dist.pairwise([paris], [lyon]) * 6371

# Returns: The distance between Paris and Lyon is 391km. sklearn=17766km
"The distance between Paris and Lyon is {0:.3g}km. sklearn={1:.5g}km".format(hdist, skdist[0][0])

score 0 · Answer 1 · answered Sep 03 '19 at 11:36

From sklearn docs:

Note that the haversine distance metric requires data in the form of [latitude, longitude] and both inputs and outputs are in units of radians.

So, convert latitude and longitude to radians before applying the function:

 skdist = dist.pairwise(np.radians([paris]), np.radians([lyon])) * 6371

How to use DistanceMetrix.pairwise() using haversine distance in scikit

1 Answers1